Mining evolutionary topic patterns in community question answering systems

Zhongfeng Zhang, Qiudan Li, Daniel Zeng

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Community Question Answering (CQA) is becoming a popular Web 2.0 application. By analyzing evolutionary topic patterns from CQA applications, one can gain insights into user interests and user responses to external events. This paper proposes a novel evolutionary topic pattern mining approach. This approach consists of three components: 1) extraction of the topics being discussed through a temporal analysis; 2) discovery of topic evolutions and construction of evolutionary graphs of extracted topics; and 3) life cycle modeling of the extracted topics. We show empirically the effectiveness of our approach using two real-world data sets.

Original languageEnglish (US)
Article number5928431
Pages (from-to)828-833
Number of pages6
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume41
Issue number5
DOIs
StatePublished - Sep 1 2011

Keywords

  • Community Question Answering (CQA)
  • evolutionary topic patterns
  • life cycle

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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